DAVID SICHINAVA, RATI SHUBLADZE
11 October 2017
Second meeting
Ethnic Minorities and the Clash of Civilizations: A Quantitative Analysis of Huntington's Thesis
An article by JONATHAN FOX (2002), that uses the Minorities at Risk dataset to provide a quantitative element to the civilizations debate
To find, whether there has been a rise in both the quantity and intensity of ethnic conflicts between groups belonging to different civilizations since the end of the Cold War.
To verify Huntington's Thesis using quantitative methods
To provide working tools for theory validation
Ever since Huntington proposed his “clash of civilizations” thesis there has been a vigorous debate over its validity.
Controversial argument that the end of the Cold War resulted in a change in the nature of world conflict
Huntington ignored some important phenomenon that will impact on conflict, thereby making his theory irrelevant.
Pfaff (1997) accuses Huntington of ignoring facts.
Hassner (1997), even goes as far as to accuse Huntington of bending the facts to fit his theory.
To assess quantitatively whether some of Huntington's predictions are correct with regard to ethnic conflict
To identify if clashes between minority and majority groups of different civilizations within the state are more common and more intense than those between groups who are both of the same civilization
To prove that Huntington was wrong, using STATISTICAL EVIDENCE
Minorities at Risk Phase 3 (MAR3) dataset
The unit of analysis in this dataset is the minority group within a state.
For each of the 275 cases there is a minority and a majority group.
Comparing mean scores of the dataset using Student's T-test
Nor does the mean intensity of civilizational vs. noncivilizational protest support Huntington?s hypothesis.
The results of this analysis do not support Huntington's “clash of civilizations”“ argument.
Civilizational conflicts constitute a minority of ethnic conflicts both during and after the Cold War.
In fact, the largest percentage of ethnic conflicts occur within civilizations
Simplistic hypothesis testing is bad for International Relations
Why validating the theories using the data matters
T-test
eList<-c("I'm the first element!", "I'm the second one", "I'm the third one")
eList[1] ### First element
eList[2] ### Second element
eList[3] ### Third element
logical<-c(TRUE, FALSE, T, F)
integer<-c(1L, 4L, 8L, 0L)
double<-c(1, 3.6, 90, 4)
characters<-c("a", "b", "სამი", "four")
simpleList<-list(TRUE, FALSE, T, F)
combiList<-list(c("a", "b"), c("იყო არაბეთს როსტევან"), c(1L, 4L, 8L, 0L), 98)
multidimList<-list(list(c("a", "b"), 7), c("რომელმან შექმნა სამყარო"))
attr(x, "Attribute") <- "Attribute"
attributes(x)
myObject<- c(10, 20, 30)
names(myObject) <- c("Age", "Height", "Weight")
eyes<-factor(c(1, 2, 3, 4), levels=c("Brown", "Green", "Blue", "Grey"))
a<-c(1, 2)
b<-c(10, 20, 30, 40, 50, 60)
ab.array<-array(c(a, b), dim=c(3, 3, 2))
print(ab.array)
a<-c(1, 2)
b<-c(10, 20, 30, 40, 50, 60)
column.name <-c("Column1", "Column2", "Column3")
row.name <- c("Row1", "Row2", "Row3")
matrix.name<- c("Thomas Anderson", "Neo")
ab.array <- array(c(a,b),dim = c(3,3,2),dimnames = list(row.name, column.name, matrix.name))
print(ab.array)
my.matrix <- matrix(c(1:15), nrow=5)
print(my.matrix)
### You could only indicate the number of columns:
my.matrix <- matrix(c(1:15), ncol=5)
print(my.matrix)
col.names <- c("First", "Second", "Third", "Fourth", "Fifth")
row.names <- c("Row1", "Row2", "Row3")
my.matrix <- matrix(c(1:15), ncol=5, dimnames=list(row.names, col.names))
print(my.matrix)
df <- data.frame(
my.id = c (1:11),
region.name = c("Samegrelo-Zemo Svaneti","Racha-Lechkhumi-Kvemo Svaneti","Imereti","Guria","Adjara", "Samtskhe-Javakheti", "Shida Kartli", "Kvemo Kartli", "Mtskheta-Mtianeti", "Tbilisi", "Kakheti"),
value.added = c(1807.4, NA, 2685.2, 584.4, 2039.7, 724.6, 1485.21, 2162.9, NA, 12147.1, 1459.1),
stringsAsFactors = FALSE
)
print(df)
x <- c(10, 2, 45, 7, 89, 11)
abs(x)
sqrt(x)
log(x)
log10(x)
exp(x)
sum(x)
median(x)
mean(x)
max(x)
min(x)
df <- data.frame(
my.id = c (1:11),
region.name = c("Samegrelo-Zemo Svaneti","Racha-Lechkhumi-Kvemo Svaneti","Imereti","Guria","Adjara", "Samtskhe-Javakheti", "Shida Kartli", "Kvemo Kartli", "Mtskheta-Mtianeti", "Tbilisi", "Kakheti"),
value.added = c(1807.4, NA, 2685.2, 584.4, 2039.7, 724.6, 1485.21, 2162.9, NA, 12147.1, 1459.1),
stringsAsFactors = FALSE
)
df[which(df$region.name == "Shida Kartli"), ] # Select only Shida Kartli
df[which(df$value.added > 1000), ] # Select those regions producing added value of more than 1 million GEL